Making the most of big data by Mark Bromwell, Director of Technology at ByBox.

Making the most of big data by Mark Bromwell, Director of Technology at ByBox.

The supply chain is, by its very nature, in a constant state of flux. However, at present, there are some especially pressing external factors speeding up this pace of change. Take Brexit, for example: CIPS recently found that some 46% of European businesses expect to reduce their use of UK suppliers as a result. It has therefore become a critical issue for the UK supply chain sector to focus on honing its services, making them demonstrably better and difficult to cut in the face of perceived cost increases. At the same time, the sector is also facing increased demand from sectors turning to technology, automation and services to meet their own increasing customer needs. The retail tech space in particular is developing rapidly, and its associated inventory and distribution needs are being placed under significant pressure to deliver.
In the face of all of these pressures, big data is one area where supply chain professionals can think differently, creatively and evolve with the times. As a society, we produce a lot of data, and the volumes get higher every day. Implementing big data correctly is just as important as collecting the data itself. This part of the conversation is often over-looked in favour of discussing the eye-catching figures. Whether already using big data to shape processes, or simply scoping out its potential, there are some tips for logistics professionals to bear in mind that can help the business truly harness the potential of big data.
Firstly, it is important to understand how big data works. A great analogy for big data compared to traditional data is the difference between a pedometer and a modern activity tracker like a Fitbit. A pedometer provides a single point of data, number of steps. From this information, you may be able to work out the number of calories burned, but it doesn’t tell you much about your overall health. A fitness tracker collects not only the number of steps but data like heart rate, time inactive, sleeping patterns and other biometrics. This data is then combined and weighted to give you a much better indication of how healthy you are.
By collecting lots of data and then combining it, a business is able to gain a much greater level of insight. In the supply chain, this can be used to enhance decision-making processes by eliminating inefficiencies, measuring success rates and providing more accurate forecasts.
The problem with big data is the bigger the data set gets, the more the people involved in the data get lost in the process. Imagine the number of people involved in the KPIs examined every day in a supply chain, and this is just one single data set. When various datasets are combined and compared for big data analysis, a number of people and departments’ work gets similarly condensed into one number. Companies will use this data to measure the performance of their staff and this can become problematic. One all-encompassing figure can mask the underlying issues. For example, when measuring customer satisfaction, you might push your engineers for a better performance when the inefficiencies in fact lie elsewhere. So maintaining perspective on what that data is truly telling you is critical.
With the rate of data growth, it’s irresponsible to create an algorithm and then forget about it. Processes in supply chains change; new data sets become available and things that you previously took for granted can become unimportant. Take the fitness tracker example again. Early Fitbits didn’t measure heart rates, but they were added in later when the data proved useful.
A good big data model should be flexible. This is especially important in forecasting. Anything which can be added to a model potentially makes it more accurate, so keeping it static will just harm your business in the long run.
We may have more data than ever before, but it is still important not to lose sight on what data really is. Big data codifies the past, it does not invent the future. The more data you have, the more accurate it becomes, but the future is still unpredictable. Big data should be used to guide you down the correct route, but not replace the intuition that made your business a success.
Data is not going away and ignoring big data carries a big risk. The potential for large data sets to change the way we view the supply chain business is huge and it is an area where an organisation cannot afford to fall behind competition. Accenture found that embedding big data analytics in operations leads to a 2.6x improvement in supply chain efficiencies – even on the scale of a smaller business that can represent significant savings.
While the implications of Brexit are still emerging, it looks likely that, regardless of the deal, supply chains will be impacted – and at just the time when they are under more pressure to deliver than ever. The sector has always had an inherent flexibility and drive to continually improve, so finding new ways to be able to compete effectively with competition, with tools such as big data, is nothing new. There are tremendous efficiencies which can be driven, along with more effective client and customer service, which may just be waiting to be discovered in a spreadsheet. Once we start to look at moving our data, not the part, we may start to find transformational solutions as well.

Leave a Comment